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» Probabilistic Document Length Priors for Language Models
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SIGIR
2010
ACM
13 years 11 months ago
Positional relevance model for pseudo-relevance feedback
Pseudo-relevance feedback is an effective technique for improving retrieval results. Traditional feedback algorithms use a whole feedback document as a unit to extract words for ...
Yuanhua Lv, ChengXiang Zhai
ICDAR
2007
IEEE
14 years 1 months ago
Fast Lexicon-Based Scene Text Recognition with Sparse Belief Propagation
Using a lexicon can often improve character recognition under challenging conditions, such as poor image quality or unusual fonts. We propose a flexible probabilistic model for c...
Jerod J. Weinman, Erik G. Learned-Miller, Allen R....
WWW
2008
ACM
14 years 8 months ago
Learning deterministic regular expressions for the inference of schemas from XML data
Inferring an appropriate DTD or XML Schema Definition (XSD) for a given collection of XML documents essentially reduces to learning deterministic regular expressions from sets of ...
Geert Jan Bex, Wouter Gelade, Frank Neven, Stijn V...
CIKM
2011
Springer
12 years 7 months ago
S3K: seeking statement-supporting top-K witnesses
Traditional information retrieval techniques based on keyword search help to identify a ranked set of relevant documents, which often contains many documents in the top ranks that...
Steffen Metzger, Shady Elbassuoni, Katja Hose, Ral...
ICML
2006
IEEE
14 years 8 months ago
Topic modeling: beyond bag-of-words
Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assu...
Hanna M. Wallach